Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS

• Published in: Advances in engineering software (1992) Vol. 67; pp. 156 - 163
• Main Authors: Yuan, Zhe, Wang, Lin-Na, Ji, Xu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2014
• Subjects: Adaptive systems; Algorithms; ANFIS; ANN; Artificial neural networks; Compressive strength; Concrete; Concrete industry; Concretes; Fuzzy logic; Genetic algorithms; Genetics; Hybrid method; Mathematical models; ANN; Concrete; Compressive strength; Hybrid method; ANFIS; Genetic algorithms
• ISSN: 0965-9978
• DOI: 10.1016/j.advengsoft.2013.09.004

•The structured and unstructured factors which affected the concrete quality were studied.•GA was used to optimize the weights and thresholds of BP-ANN.•For the ANFIS two building methods were explored and promoted the application in engineering.•GA based BP-ANN and ANFIS have a better performance than regression models and BP-ANN. The management of concrete quality is an important task of concrete industry. This paper researched on the structured and unstructured factors which affect the concrete quality. Compressive strength of concrete is one of the most essential qualities of concrete, conventional regression models to predict the concrete strength could not achieve an expected result due to the unstructured factors. For this reason, two hybrid models were proposed in this paper, one was the genetic based algorithm the other was the adaptive network-based fuzzy inference system (ANFIS). For the genetic based algorithm, genetic algorithm (GA) was applied to optimize the weights and thresholds of back-propagation artificial neural network (BP-ANN). For the ANFIS model, two building methods were explored. By adopting these predicting methods, considerable cost and time-consuming laboratory tests could be saved. The result showed that both of these two hybrid models have good performance in desirable accuracy and applicability in practical production, endowing them high potential to substitute the conventional regression models in real engineering practice.